1. Field of the Invention
The present invention relates to a method for multi-layer classifier and, more particularly, to a method for classifier, which can be used to built a multi-layer discriminant analysis model, and determine attributes and cut-points.
2. Description of Related Art
Classification methods are widely applied in various fields. For example, in the financial industry, the classification method can be used for predicting the probability of exerting irrecoverable loans when examining on an applicant for credit cards. In the medical practice, the classification method can be used for determining whether the tissue is normal or not. Furthermore, in the marketing research, the classification method can determine whether the marketing strategy can attract consumers' attention and increase consumption of goods or not. Hence, the classification methods play an important role in the research on data mining.
Among all of the classification methods, proper attributes must be selected to build a classification model. During the process of classification model building, the data are classified into two groups, wherein one group consists of training samples, and the other group consists of independent test samples. In addition, the training samples are used to build a classification model, and the independent test samples are used to verify the robustness of the classification model.
Currently, there are two kinds of common classification methods. One method is Fisher linear discriminant analysis (FLD), and the other one is classification and regression trees (CART). However, since parts of attributes can only be used to determine specific classes, the accuracy of the aforementioned classification method is decreased. The accuracy is probably decreased because of different combination of attributes selection, and lacks of evaluating a performance of the discriminant analysis model.
Hence, it is desirable to provide a novel method for multi-layer classifier to solve the aforementioned problems.